Single Image, Context Aware Action Estimation in Sports. - Christian Lanius; Daisuke Kobayashi; Kazushige Ouchi; Yoshimitsu Aoki (SITIS, 2018)

NEUROTEC I: Neuro-inspired Artificial Intelligence Technologies for the Electronics of the Future. - Melvin Galicia; Stephan Menzel; Farhad Merchant; Maximilian Müller; Hsin-Yu Chen; Qing-Tai Zhao; Felix Cüppers; Abdur R. Jalil; Qi Shu; Peter Schüffelgen; Gregor Mussler; Carsten Funck; et al. (DATE, 2022)

All-Digital Time-Domain Compute-in-Memory Engine for Binary Neural Networks With 1.05 POPS/W Energy Efficiency. - Jie Lou; Christian Lanius; Florian Freye; Tim Stadtmann; Tobias Gemmeke (ESSCIRC, 2022)

Multi-Function CIM Array for Genome Alignment Applications built with Fully Digital Flow. - Christian Lanius; Tobias Gemmeke (NorCAS, 2022)

Scalable Time-Domain Compute-in-Memory BNN Engine with 2.06 POPS/W Energy Efficiency for Edge-AI Devices. - Jie Lou; Florian Freye; Christian Lanius; Tobias Gemmeke (ACM Great Lakes Symposium on VLSI, 2023)

Hardware Trojans in fdSOI. - Christian Lanius; Florian Freye; Shutao Zhang; Tobias Gemmeke (ISLPED, 2023)

Automatic Generation of Structured Macros Using Standard Cells ‒ Application to CIM. - Christian Lanius; Jie Lou; Johnson Loh; Tobias Gemmeke (ISLPED, 2023)

An Energy-Efficient and Area-Efficient Depthwise Separable Convolution Accelerator with Minimal On-Chip Memory Access. - Yi Chen; Jie Lou; Christian Lanius; Florian Freye; Johnson Loh; Tobias Gemmeke (VLSI-SoC, 2023)

A Unified and Energy-Efficient Depthwise Separable Convolution Accelerator. - Yi Chen; Jie Lou; Christian Lanius; Florian Freye; Johnson Loh; Tobias Gemmeke (VLSI-SoC (Selected Papers), 2023)

nAIxt: A Light-Weight Processor Architecture for Efficient Computation of Neuron Models. - Kevin Kauth; Christian Lanius; Tobias Gemmeke (ARCS, 2024)

Merits of Time-Domain Computing for VMM - A Quantitative Comparison. - Florian Freye; Jie Lou; Christian Lanius; Tobias Gemmeke (ISQED, 2024)

Efficient ASIC Architecture for Low Latency Classic McEliece Decoding. - Daniel Fallnich; Christian Lanius; Shutao Zhang; Tobias Gemmeke (IACR Trans. Cryptogr. Hardw. Embed. Syst., 2024)

An Energy Efficient All-Digital Time-Domain Compute-in-Memory Macro Optimized for Binary Neural Networks. - Jie Lou; Florian Freye; Christian Lanius; Tobias Gemmeke (IEEE Trans. Circuits Syst. I Regul. Pap., 2024)

Fully Digital, Standard-Cell-Based Multifunction Compute-in-Memory Arrays for Genome Sequencing. - Christian Lanius; Tobias Gemmeke (IEEE Trans. Very Large Scale Integr. Syst., 2024)

GUPA: Group-Wise Uniform Pruning Accelerator for Depthwise Separable Convolution. - Yi Chen; Malte Wabnitz; Jie Lou; Christian Lanius; Tobias Gemmeke (COOL CHIPS, 2025)

A 1.27 fJ/B/transition Digital Compute-in-Memory Architecture for Non-Deterministic Finite Automata Evaluation. - Christian Lanius; Florian Freye; Tobias Gemmeke (ACM Great Lakes Symposium on VLSI, 2025)

A 22nm 96.83-TOPS/W Time-Domain Compute-in-Memory Engine Utilizing Mixed-Fidelity for Edge-AI Applications. - Jie Lou; Florian Freye; Christian Lanius; Tobias Gemmeke (ACM Great Lakes Symposium on VLSI, 2025)

An All-Digital Time-Domain Compute-in-Memory Engine for Convolutional Neural Networks in 22nm. - Jie Lou; Florian Freye; Christian Lanius; Tobias Gemmeke (ISCAS, 2025)